

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}

class     __snake_case    (				__lowerCAmelCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"camembert"


       def __init__(		self      , _UpperCamelCase=3_05_22      , _UpperCamelCase=7_68      , _UpperCamelCase=12      , _UpperCamelCase=12      , _UpperCamelCase=30_72      , _UpperCamelCase="gelu"      , _UpperCamelCase=0.1      , _UpperCamelCase=0.1      , _UpperCamelCase=5_12      , _UpperCamelCase=2      , _UpperCamelCase=0.02      , _UpperCamelCase=1E-12      , _UpperCamelCase=1      , _UpperCamelCase=0      , _UpperCamelCase=2      , _UpperCamelCase="absolute"      , _UpperCamelCase=True      , _UpperCamelCase=None      , **_UpperCamelCase      , )      ->							str:






              """simple docstring"""


              super().__init__(pad_token_id=lowerCamelCase_      , bos_token_id=lowerCamelCase_      , eos_token_id=lowerCamelCase_      , **lowerCamelCase_   )

              __snake_case       	=							vocab_size
              __snake_case       	=							hidden_size
              __snake_case       	=							num_hidden_layers
              __snake_case       	=							num_attention_heads
              __snake_case       	=							hidden_act
              __snake_case       	=							intermediate_size
              __snake_case       	=							hidden_dropout_prob
              __snake_case       	=							attention_probs_dropout_prob
              __snake_case       	=							max_position_embeddings
              __snake_case       	=							type_vocab_size
              __snake_case       	=							initializer_range
              __snake_case       	=							layer_norm_eps
              __snake_case       	=							position_embedding_type
              __snake_case       	=							use_cache
              __snake_case       	=							classifier_dropout






class     __snake_case    (				__lowerCAmelCase  ):
       """simple docstring"""







       @property
       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              if self.task == "multiple-choice":
                     __snake_case       	=							{0: """batch""", 1: """choice""", 2: """sequence"""}
              else:
                     __snake_case       	=							{0: """batch""", 1: """sequence"""}
              return OrderedDict(
                  [
                      ("""input_ids""", dynamic_axis),
                      ("""attention_mask""", dynamic_axis),
                  ]   )


# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
